Fine-scaled optical detection of nitrogen stress in grain crops

Mieke Reyniers; Els Vrindts; Josse de Baerdemaeker; Pol Darius

doi:10.1117/12.510648

24 February 2004 Fine-scaled optical detection of nitrogen stress in grain crops

Mieke Reyniers, Els Vrindts, Josse de Baerdemaeker, Pol Darius

Proceedings Volume 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V; (2004) https://doi.org/10.1117/12.510648
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

What is lacking in precision farming at present are more comprehensive and fast non-destructive methods for obtaining the data needed to prescribe varia*ble treatments. In precision farming there is a demand for sensors that can easily monitor crop nitrogen requirements throughout the growing season on a high resolution. Currently used optical measurement platforms like satellites, airplanes and hand-held sensors, do not meet the needs of precision agriculture for good nitrogen management possibilities. An automated sensor system mounted on a tractor was developed and used to detect crop nitrogen status optically. A line spectrograph was used to detect amount of nitrogen (kgN/ha) and chlorophyll (kg/ha) in a wheat crop (Triticum aestivum L.). By calculating the red edge inflection point of the plant spectra in the images, wheat crop nitrogen stress within small areas in the field could be detected. Spectrograph red edge was highly correlated with applied nitrogen to the wheat crop (0.90), with crop nitrogen uptake (0.89) and with chlorophyll amount in the crop (0.86). The average errors when estimating those variables with the red edge inflection point were -0.4% (24.15kgN/ha), -1% (17.25kgN/ha) and -10% (14.74kg/ha) respectively. This means that spectrograph red edge measurements of the wheat crop during the growing season can be a predictor of topdress nitrogen needs.

Citation Download Citation

Mieke Reyniers, Els Vrindts, Josse de Baerdemaeker, and Pol Darius "Fine-scaled optical detection of nitrogen stress in grain crops", Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); https://doi.org/10.1117/12.510648

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